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Embodied Carbon: A call to the building industry

2022· article· en· W4312192000 on OpenAlexaff
Daniel R. Rondinel-Oviedo, Naomi Keena

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsMcGill University
Fundersnot available
KeywordsBuilt environmentBenchmarkingProcess (computing)Architectural engineeringEngineeringBusinessEnvironmental resource managementEnvironmental scienceComputer scienceCivil engineering

Abstract

fetched live from OpenAlex

Abstract The demand for building materials will increase drastically in the following decades due to urbanization and population growth. Buildings generate almost 40% of annual global carbon dioxide (CO 2 ) emissions contributing to human-induced climate change. Of this fraction, 11% is associated with material production and building construction, and 28% is contributed to the operational activities of a building (heating, cooling, lighting). One primary measure of the climate impact of buildings is embodied carbon (EC). This paper aims to find the recent developments in relation to embodied carbon within the building industry. To achieve this goal, a literature review was conducted to study the EC concept concerning materials and construction. The article explores and presents the state-of-the-art of diverse research and development related to this concept, focusing primarily on (1) the carbon cycle and the building sector, (2) EC benchmarking, (3) bio-based materials, and (4) other low-carbon material alternatives such as the development of carbon capture technologies. The objective of this paper is to summarize current and emerging trends as well as research priorities and tools to inform designers and engineers and facilitate their decision-making during the design process. It also aims to facilitate in advancing the creation of regulations and policies toward natural-based material solutions and the development of low-carbon high-tech material technologies deemed central for a sustainable built environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.220
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2022
Admission routes1
Has abstractyes

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